The App Modernisation Trap (And How AI Gets You Out)

If you're a CIO or CTO sitting on legacy enterprise systems, you're not wrong to be cautious.

Most organisations are running critical platforms on aging frameworks, on-prem infrastructure, and architectures built for a different era. The case for modernisation is clear, but for years the path forward has looked like a 9 -18 month programs. High cost. Higher risk.

So most application portfolios stay exactly where they are accumulating technical debt, security vulnerabilities, and rising support costs.

What's changed is AI-assisted modernisation.

AI is not going to magically do all the work for you. AI will act as a force multiplier-where experienced engineers can now move 3-4x faster on the mechanical parts of migration, freeing them to focus on architecture, risk, and business logic.

When you combine AI tooling with modern cloud patterns and automated testing, what used to take 12 months can now be done in 8-12 weeks for mid-sized applications (50-200K lines of code, 5-15 services).

Here's the approach we use with clients.

1️⃣ Start with facts, not assumptions

Before touching code, use cloud migration assessment tools to map the portfolio:

• What can be upgraded with minimal changes

• What dependencies are blocking modernisation

• What should move to cloud services or stay on-prem

• Where the integration and data migration risks actually are

This produces a migration roadmap in days, not weeks-and removes the guesswork that derails most programs three months in.

2️⃣ Use AI to accelerate the mechanical work

Once you have a plan, AI coding assistants become the accelerator for the high-volume, low-risk refactoring:

• Converting legacy UI frameworks to modern web technologies

• Replacing outdated service protocols with REST APIs or event-driven patterns

• Updating dependency management, logging, and configuration approaches

• Aligning code with modern security standards

A senior engineer can now refactor thousands of lines of boilerplate in days instead of months. But-and these matters-they're still reviewing every change, especially around business logic and data access.

3️⃣ Lock in behavior before you change anything

The biggest risk in modernisation isn't the code-it's breaking the business.

Before refactoring, use automated testing frameworks to capture how the system behaves today. AI can generate the test scaffolding, but humans define what "correct" looks like.

This creates a safety net: if the modernised system passes the same tests, you know it behaves the same way. That turns a risky rewrite into a controlled, test-driven upgrade.

4️⃣ Move to cloud services strategically

Instead of lifting technical debt into the cloud, modernise it on its way to the cloud (if it makes sese):

• Databases → managed database services

• Background jobs → serverless functions or container platforms

• Identity → cloud identity providers

• Applications → managed container services or platform-as-a-service

The goal isn't "cloud for cloud's sake"- it's reducing operational overhead while improving security, scalability, and cost predictability.

The outcome

Platforms that were high-risk and expensive to run become modern, cloud-ready systems that are easier to change and cheaper to operate.

And because AI handles the mechanical heavy lifting, what used to require a 12-month transformation can now be delivered in 8-12 weeks, assuming you have senior engineering talent driving the work and clear architectural decisions upfront.

That's not a silver bullet. But it is a fundamentally different cost-risk-speed equation than existed 18 months ago.

What this requires

To be clear: this approach works when you have:

• Senior engineers who understand both legacy and modern patterns

• Executive commitment to architectural decisions (not design-by-committee)

• Automated testing infrastructure (or willingness to build it)

• A system that's well-understood, even if it's outdated

If your application portfolio is poorly documented, lacks any test coverage, or has deep integration with proprietary systems, the timeline stretches. But even then, AI tooling still makes the work faster and less risky than traditional approaches.